import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 0
# The number of cities in the core net
NC = 3
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 7667, C = 5292

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 1640 | is_connectable
   82 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  123 | is_connected_step
 3362 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 600.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 600.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 5292 rows, 7667 columns and 25297 nonzeros
Model fingerprint: 0x44b6d43b
Model has 820 general constraints
Variable types: 2460 continuous, 5207 integer (5207 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 89 rows and 1768 columns
Presolve time: 0.08s
Presolved: 5203 rows, 5899 columns, 25440 nonzeros
Variable types: 2460 continuous, 3439 integer (3439 binary)
Found heuristic solution: objective 76211.586734

Root relaxation: objective 1.196505e+03, 5276 iterations, 0.12 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 1196.50494    0  140 76211.5867 1196.50494  98.4%     -    0s
H    0     0                    38744.687726 1196.50494  96.9%     -    0s
     0     0 2709.97670    0  221 38744.6877 2709.97670  93.0%     -    0s
     0     0 2740.62084    0  239 38744.6877 2740.62084  92.9%     -    0s
H    0     0                    21181.958577 2740.62084  87.1%     -    0s
     0     0 2742.96585    0  231 21181.9586 2742.96585  87.1%     -    0s
     0     0 2743.17269    0  238 21181.9586 2743.17269  87.0%     -    0s
     0     0 2785.20392    0  341 21181.9586 2785.20392  86.9%     -    0s
     0     0 2787.60608    0  343 21181.9586 2787.60608  86.8%     -    0s
     0     0 2788.75829    0  334 21181.9586 2788.75829  86.8%     -    0s
     0     0 2788.77649    0  327 21181.9586 2788.77649  86.8%     -    0s
     0     0 2808.89172    0  268 21181.9586 2808.89172  86.7%     -    1s
H    0     0                    21029.629265 2808.89172  86.6%     -    1s
     0     0 2818.40848    0  328 21029.6293 2818.40848  86.6%     -    1s
     0     0 2819.24128    0  326 21029.6293 2819.24128  86.6%     -    1s
     0     0 2819.27480    0  330 21029.6293 2819.27480  86.6%     -    1s
     0     0 2832.79742    0  346 21029.6293 2832.79742  86.5%     -    1s
     0     0 2839.99093    0  366 21029.6293 2839.99093  86.5%     -    1s
     0     0 2842.16565    0  367 21029.6293 2842.16565  86.5%     -    1s
     0     0 2842.19274    0  369 21029.6293 2842.19274  86.5%     -    1s
     0     0 2845.90998    0  407 21029.6293 2845.90998  86.5%     -    1s
     0     0 2846.43946    0  408 21029.6293 2846.43946  86.5%     -    1s
     0     0 2850.39054    0  411 21029.6293 2850.39054  86.4%     -    1s
     0     0 2850.69205    0  412 21029.6293 2850.69205  86.4%     -    1s
     0     0 2851.58484    0  395 21029.6293 2851.58484  86.4%     -    1s
     0     0 2851.58484    0  231 21029.6293 2851.58484  86.4%     -    2s
H    0     0                    18491.570643 2871.98667  84.5%     -    2s
     0     2 2871.98667    0  231 18491.5706 2871.98667  84.5%     -    2s
H   73    63                    17002.343259 3232.02967  81.0%   330    4s
H  103    81                    16837.031118 3232.02967  80.8%   279    4s
H  115    85                    16677.512666 3232.02967  80.6%   256    5s
H  116    85                    16480.013577 3232.02967  80.4%   256    5s
H  121    85                    16267.498262 3232.02967  80.1%   263    5s
H  142   102                    16188.364911 3232.02967  80.0%   260    5s
H  151   108                    15909.279956 3232.02967  79.7%   262    5s
H  229   154                    15840.094834 3232.02967  79.6%   220    6s
H  230   154                    15706.358301 3232.02967  79.4%   220    6s
H  526   343                    15702.395387 3232.02967  79.4%   168    7s
H  580   358                    15702.365318 3232.02967  79.4%   170    8s
H  582   358                    15658.932881 3232.02967  79.4%   171    8s
H  599   358                    15647.179006 3232.02967  79.3%   173    8s
H  649   385                    15518.933207 3232.02967  79.2%   170    8s
H  797   422                    15518.933192 3232.02967  79.2%   160    9s
  1024   457 5587.41187   63  213 15518.9332 3232.02967  79.2%   159   10s
H 1088   457                    15518.933081 3232.02967  79.2%   154   10s
H 1224   499                    15518.933054 3232.02967  79.2%   149   11s
  1910   889 12727.1682  113  262 15518.9331 3232.02967  79.2%   127   15s
  1924   900 3232.02967   15  264 15518.9331 3232.02967  79.2%   136   22s
  1940   905 4208.32250   18  345 15518.9331 3232.02967  79.2%   143   25s
H 1993   866                    15518.933046 3674.25063  76.3%   151   27s
H 2093   806                    15518.933023 4121.62445  73.4%   156   29s
  2140   789 5726.35151   28  198 15518.9330 4222.65069  72.8%   159   30s
H 2184   745                    15518.932992 4222.65069  72.8%   161   30s
H 2186   712                    15518.932938 4222.65069  72.8%   161   31s
H 2193   673                    15518.932928 4222.65069  72.8%   162   31s
  2762   696 5355.94864   22  270 15518.9329 4828.37638  68.9%   165   35s
H 3590   636                    15476.122400 6425.61572  58.5%   165   39s
  3592   702 6554.88524   31  211 15476.1224 6425.61572  58.5%   165   40s
  4696   804 9024.27555   30  166 15476.1224 8098.97839  47.7%   165   45s
H 4959   786                    15476.122388 8609.32282  44.4%   164   45s
  5984   478 infeasible   34      15476.1224 10744.7230  30.6%   163   50s
 13703  1731     cutoff   51      15476.1224 15401.9399  0.48%  82.0   55s
H13943  1731                    15476.122325 15401.9619  0.48%  80.6   55s
H18550  1337                    15476.122314 15426.8792  0.32%  62.0   58s
 21697   573 15460.5608   45   27 15476.1223 15446.3044  0.19%  53.7   60s

Cutting planes:
  Gomory: 34
  Cover: 125
  Implied bound: 19
  MIR: 60
  Flow cover: 164
  GUB cover: 1
  Inf proof: 3
  Zero half: 16
  RLT: 97
  Relax-and-lift: 28

Explored 24057 nodes (1186935 simplex iterations) in 60.77 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 15476.1 15518.9 15518.9 ... 15702.4

Optimal solution found (tolerance 1.00e-04)
Best objective 1.547612231358e+04, best bound 1.547612231358e+04, gap 0.0000%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.5	4.000000e+00	8.500000
1	1	Borås	False	False	0.7	1.030000e+01	10.999999
2	2	Eskilstuna	True	False	1.1	6.190000e+01	63.000000
3	3	Falun	False	False	2.3	6.021628e-12	2.300000
4	4	Gävle	False	False	1.0	3.130000e+01	32.300000
5	5	Göteborg	False	False	5.8	5.240253e-13	5.800000
6	6	Halmstad	False	False	3.4	6.099999e+00	9.499999
7	7	Haparanda	False	False	4.6	0.000000e+00	4.600000
8	8	Helsingborg	False	False	2.2	3.900000e+00	6.099999
9	9	Hudiksvall	False	False	3.9	2.740000e+01	31.300000
10	10	Jönköping	False	False	1.2	2.389866e-12	1.200000
11	11	Kalmar	False	False	4.0	5.613288e-13	4.000000
12	12	Karlskrona	False	False	1.6	8.077983e-13	1.600000
13	13	Karlstad	False	False	0.9	6.516565e-12	0.900000
14	14	Kiruna	False	False	4.0	0.000000e+00	4.000000
15	15	Kristianstad	False	False	3.0	5.103743e-07	3.000001
16	16	Lidköping	False	False	1.2	1.340000e+01	14.600000
17	17	Linköping	False	False	4.1	1.800000e+00	5.900000
18	18	Luleå	False	False	2.0	1.310000e+01	15.100000
19	19	Malmö	False	False	2.6	1.300000e+00	3.900001
20	20	Motala	False	False	2.4	2.010000e+01	22.500000
21	21	Norrköping	False	False	1.7	9.849899e-13	1.700000
22	22	Nyköping	False	False	4.6	7.915446e-12	4.600000
23	23	Sandviken	False	False	4.5	7.140954e-13	4.500000
24	24	Skellefteå	False	False	4.4	1.510000e+01	19.500000
25	25	Skövde	False	False	2.5	1.100000e+01	13.500000
26	26	Stockholm	False	False	7.0	5.684342e-13	7.000000
27	27	Sundsvall	False	False	0.7	2.670000e+01	27.400000
28	28	Trelleborg	False	False	1.3	3.749556e-12	1.300000
29	29	Uddevalla	False	False	4.0	5.800000e+00	9.800000
30	30	Umeå	False	False	3.2	1.950000e+01	22.700000
31	31	Uppsala	False	False	1.9	3.230000e+01	34.200000
32	32	Varberg	False	False	0.8	9.499999e+00	10.300000
33	33	Vetlanda	False	False	2.1	1.090000e+01	13.000001
34	34	Vänersborg	False	False	3.6	9.800000e+00	13.400000
35	35	Västervik	False	False	1.8	1.492140e-12	1.800000
36	36	Västerås	True	False	1.4	1.110000e+02	112.400000
37	37	Växjö	False	False	2.3	4.600001e+00	6.900001
38	38	Örebro	True	True	4.1	1.639000e+02	55.600000
39	39	Örnsköldsvik	False	False	3.1	2.270000e+01	25.800000
40	40	Östersund	False	False	0.9	1.596057e-12	0.900000

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	19.500000	731.139160	111
1	Eskilstuna	Västerås	CORE	63.000000	430.000000	43
2	Gävle	Hudiksvall	SUB	-31.300000	1278.724564	118
3	Skövde	Örebro	SUB	13.500000	657.432739	132
4	Lidköping	Örebro	SUB	14.600000	841.276343	148
5	Stockholm	Västerås	SUB	7.000000	234.688115	101
6	Karlskrona	Växjö	SUB	1.600000	62.121900	102
7	Motala	Örebro	SUB	22.500000	637.862087	92
8	Boden	Kiruna	SUB	-4.000000	637.913000	291
9	Sundsvall	Östersund	SUB	-0.900000	70.870188	166
10	Halmstad	Helsingborg	SUB	-6.099999	145.447326	79
11	Umeå	Örnsköldsvik	SUB	22.700000	849.479945	111
12	Boden	Luleå	SUB	8.500000	58.656839	32
13	Eskilstuna	Örebro	CORE	-55.600000	830.000000	83
14	Karlstad	Örebro	SUB	0.900000	29.229970	77
15	Halmstad	Varberg	SUB	9.499999	167.432219	65
16	Lidköping	Vänersborg	SUB	-13.400000	206.938975	60
17	Uddevalla	Vänersborg	SUB	9.800000	39.823253	21
18	Linköping	Motala	SUB	5.900000	77.952787	51
19	Sandviken	Västerås	SUB	4.500000	174.172927	110
20	Falun	Västerås	SUB	2.300000	115.327746	128
21	Linköping	Västervik	SUB	-1.800000	64.378861	97
22	Sundsvall	Örnsköldsvik	SUB	-25.800000	1177.683887	127
23	Jönköping	Motala	SUB	1.200000	52.590906	108
24	Eskilstuna	Nyköping	SUB	-4.600000	119.995513	83
25	Vetlanda	Växjö	SUB	-6.900001	143.305438	72
26	Borås	Varberg	SUB	-10.300000	260.758771	84
27	Haparanda	Luleå	SUB	4.600000	210.858555	124
28	Luleå	Skellefteå	SUB	15.100000	742.410242	133
29	Kristianstad	Växjö	SUB	3.000001	134.707658	120
30	Motala	Vetlanda	SUB	-13.000000	654.828175	135
31	Uppsala	Västerås	SUB	34.200000	755.020135	78
32	Gävle	Uppsala	SUB	32.300000	484.711112	60
33	Helsingborg	Malmö	SUB	-3.900000	67.318052	60
34	Göteborg	Uddevalla	SUB	5.800000	117.417503	70
35	Västerås	Örebro	CORE	112.400000	930.000000	93
36	Malmö	Trelleborg	SUB	-1.300000	18.150077	34
37	Hudiksvall	Sundsvall	SUB	-27.400000	641.652314	81
38	Eskilstuna	Norrköping	SUB	-1.700000	65.379519	102
39	Borås	Skövde	SUB	10.999999	384.262758	105
40	Kalmar	Vetlanda	SUB	4.000000	174.202753	119

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  2190.000
subnet = 13286.122
------------------
total  = 15476.122

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))